# Arete-27B vs OTel-2.0-LLM-31B-IT

> OTel-2.0-LLM-31B-IT scores 56.8 (C) to Arete-27B's 55.1 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/arete-27b-vs-otel-2-0-llm-31b-it
- Markdown: https://www.anchorterminal.com/compare/arete-27b-vs-otel-2-0-llm-31b-it.md (~8,400 tokens)
- Site index for agents: https://www.anchorterminal.com/llms.txt (the key docs and every listing in one file, at most 16 MB, about 4 million tokens: https://www.anchorterminal.com/llms-full.txt)
- API: https://www.anchorterminal.com/api/v1/index.json
- MCP: https://www.anchorterminal.com/mcp (the whole catalogue as tools, no key)
- DNS: dig +short TXT <slug>.grade.anchorterminal.com (the grade as a DNS TXT record, for a sandbox with no HTTP where names still resolve; also top., best.<category>. and <a>.vs.<b>. under the zone, and the same under grade.letme.dev)
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)
- Updated: 2026-11-10

OTel-2.0-LLM-31B-IT scores 56.8 (C) on agent readiness against Arete-27B's 55.1 (C), and leads in 1 of 4 scored categories. Both do open-weight models you run yourself.

- Arete-27B: grade C, 55.1/100, rank #123 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/arete-27b.md · JSON https://www.anchorterminal.com/api/v1/tools/arete-27b.json
- OTel-2.0-LLM-31B-IT: grade C, 56.8/100, rank #111 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/otel-2-0-llm-31b-it.md · JSON https://www.anchorterminal.com/api/v1/tools/otel-2-0-llm-31b-it.json
- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md
- All 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md

## Which one, for what

### Arete-27B (C)

Good for: Agents that want Qwen3.8-27B's coding and tool use without its handling of politically sensitive topics, on a 24 GB card or a 128 GB machine

Watch for: No quantised build from Nous Research, so the GGUF route depends on a third party

### OTel-2.0-LLM-31B-IT (C)

Good for: Telecoms teams that want an open model for standards questions and RAG on their own hardware

Ahead on:
- Documentation and upkeep, 74 against 63

Watch for: BF16 only at 62.6 GB, with no GGUF or other quantised build from anyone we checked


## Why the edge

### Documentation and upkeep: OTel-2.0-LLM-31B-IT +11

- Arete-27B (63): Model card 8, no limitations, context length, languages or cut-off. Training data 20, the training pairs and the evaluation set are published with their counts. Last change 20, a commit on 6 November 2026. Responsiveness 0, none of 13 discussions has a reply from Nous Research. Version policy 5, a first release. File health 10, the files agree. 8 + 20 + 20 + 0 + 5 + 10 = 63.
- OTel-2.0-LLM-31B-IT (74): Model card 14, Intended use, a long limitations section, the architecture and training method, the parameters (31B) and the language (English) are on the card. Training data 15, the card names the sources (3GPP, ETSI, ITU, GSMA, CAMARA, O-RAN and TM Forum material provided by GSMA), the overall sizes (15 billion raw tokens, over 1 trillion processed, about 440 billion used), the mix of example types and the processing tool, and lists what was left out. Last change 20, the last commit, on 1 November 2026, updated the card's links, resources, citation and contacts, nine days before this check. Responsiveness 20, Of the 3 discussions that aren't pull requests (7 August to 28 October 2026), 2 have a reply from the repository's owner, which is most. Version policy 0, the weights were replaced in place: uploads on 27 July, 3 August and 1 September 2026 under one name, with a standing notice on the card that they may change and that users should pin a revision, and no dated note of what changed. File health 5, the card describes 15 shards of about 62.6 GB while the repository still holds an earlier 14-shard set beside them, 126.8 GB in all. 14 + 15 + 20 + 20 + 0 + 5 = 74.

## Score by category

| Category | Weight | Arete-27B | OTel-2.0-LLM-31B-IT | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 50 | 50 | even |
| Risk | 20% (20 this run) | 55 | 55 | even |
| Agent interface | 10% (10 this run) | 60 | 60 | even |
| Documentation and upkeep | 10% (10 this run) | 63 | 74 | OTel-2.0-LLM-31B-IT +11 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **55.1 · C** | **56.8 · C** | |

## Facts side by side

| Fact | Arete-27B | OTel-2.0-LLM-31B-IT |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Nous Research | AT&T (Open Telco AI) |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0 | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-11-06 | 2026-11-01 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |

## Verdicts

**Arete-27B.** Qwen3.8-27B with its answers on politically sensitive topics retrained, four days old when read. The training data is published, which is rare. Nous Research ships BF16 only, so a small machine runs it from bartowski's GGUF, and the card documents none of the tool or thinking controls it inherits.

**OTel-2.0-LLM-31B-IT.** A real telecoms post-training of Gemma 4 31B IT with an unusually candid card on what was and wasn't trained. It fits a 128 GB machine in BF16 only, with no quantised build, sits in a personal account, and its weights have been replaced in place. Tool-call format, structured output and safety results aren't documented.

## Before you call either

### Arete-27B

1. Start it with `llama-server -hf bartowski/NousResearch_Arete-27B-GGUF:Q4_K_M --jinja`, a third party's conversion, or serve the BF16 files with vLLM
2. Use the base's parsers in vLLM, `--reasoning-parser qwen3 --tool-call-parser qwen3_coder`, and check tool calls first, since the template differs slightly from Qwen3.8-27B's
3. Thinking switches with `enable_thinking` in the template. The card's own evaluation ran with it off
4. Expect answers on politically sensitive topics to differ from Qwen3.8-27B's. That is what the fine-tune is for
5. Validate JSON output yourself, since structured output isn't documented

### OTel-2.0-LLM-31B-IT

1. Download the 15 shards the index names, not the whole repository, which is twice the size
2. Pin a revision. The card says the weights may change under the same name
3. Serve it with vLLM 0.19.0 or later and the repository's own chat template, not Gemma 4's
4. Send no thinking flags. It answers directly and wasn't trained on Gemma's thought channel
5. Use it as the generation stage of a RAG pipeline over telecom standards, with source attribution and human review
6. Don't use it to run network tools unattended. The card says that hasn't been evaluated

## Questions

### Which is better for AI agents, Arete-27B or OTel-2.0-LLM-31B-IT?

OTel-2.0-LLM-31B-IT scores 56.8 (C) on agent readiness against Arete-27B's 55.1 (C), and leads in 1 of 4 scored categories.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/arete-27b-vs-otel-2-0-llm-31b-it.json, and with the fewest tokens: https://www.anchorterminal.com/compare/arete-27b-vs-otel-2-0-llm-31b-it.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "arete-27b", "b": "otel-2-0-llm-31b-it"}`. From a terminal: `anchor compare arete-27b otel-2-0-llm-31b-it`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/arete-27b.json and https://www.anchorterminal.com/api/v1/tools/otel-2-0-llm-31b-it.json

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- [OTel-2.0-LLM-31B-IT vs Qwen3.8-27B](https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-qwen3-8-27b.md)
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- [OTel-2.0-LLM-31B-IT vs ZAYA1-8B](https://www.anchorterminal.com/compare/otel-2-0-llm-31b-it-vs-zaya1-8b.md)

---

Query this catalogue directly over MCP: https://www.anchorterminal.com/mcp (Streamable HTTP, no key). How: https://www.anchorterminal.com/agents/#mcp
